Statistical Parametric Mapping: The Analysis of Functional Brain Images
Published on 2. November 2006
Book
Hardback
688 pages
978-0-12-372560-8 (ISBN)
Description
In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Scientists actively involved in neuroimaging research and the analysis of data, as well as students at a masters and doctoral level studying cognitive neuroscience and brain imaging.
Product notice
sewn/stitched
Paper over boards
Dimensions
Height: 284 mm
Width: 222 mm
Thickness: 40 mm
Weight
2136 gr
ISBN-13
978-0-12-372560-8 (9780123725608)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
William D. Penny | Karl J. Friston | John T. Ashburner
Statistical Parametric Mapping: The Analysis of Functional Brain Images
Book
11/2006
Academic Press
€182.15
Article exhausted; check different version
Persons
Editor
Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK
Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK
Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK
Content
Part 1: Introduction
Chapter 1: A short history of SPM
Chapter 2: Statistical parametric mapping
Chapter 3: Modelling brain responses
Part 2: Computational anatomy
Chapter 4: Rigid Body Registration
Chapter 5: Non-linear Registration
Chapter 6: Segmentation
Chapter 7: Voxel-Based Morphometry
Part 3: General linear models
Chapter 8: The General Linear Model
Chapter 9: Contrasts and Classical Inference
Chapter 10: Covariance Components
Chapter 11: Hierarchical Models
Chapter 12: Random Effects Analysis
Chapter 13: Analysis of Variance
Chapter 14: Convolution Models for fMRI
Chapter 15: Efficient Experimental Design for fMRI
Chapter 16: Hierarchical models for EEG and MEG
Part 4: Classical inference
Chapter 17: Parametric procedures
Chapter 18: Random Field Theory
Chapter 19: Topological Inference
Chapter 20: False Discovery Rate procedures
Chapter 21: Non-parametric procedures
Part 5: Bayesian inference
Chapter 22: Empirical Bayes and hierarchical models
Chapter 23: Posterior probability maps
Chapter 24: Variational Bayes
Chapter 25: Spatio-temporal models for fMRI
Chapter 26: Spatio-temporal models for EEG
Part 6: Biophysical models
Chapter 27: Forward models for fMRI
Chapter 28: Forward models for EEG
Chapter 29: Bayesian inversion of EEG models
Chapter 30: Bayesian inversion for induced responses
Chapter 31: Neuronal models of ensemble dynamics
Chapter 32: Neuronal models of energetics
Chapter 33: Neuronal models of EEG and MEG
Chapter 34: Bayesian inversion of dynamic models
Chapter 35: Bayesian model selection and averaging
Part 7: Connectivity
Chapter 36: Functional integration
Chapter 37: Functional connectivity: eigenimages and multivariate analyses
Chapter 38: Effective Connectivity
Chapter 39: Non-linear coupling and kernels
Chapter 40: Multivariate autoregressive models
Chapter 41: Dynamic Causal Models for fMRI
Chapter 42: Dynamic causal models for EEG
Chapter 43: Dynamic Causal Models and Bayesian selection
Appendices
Linear models and inference
Dynamical systems
Expectation maximization
Variational Bayes under the Laplace approximation
Kalman filtering
Random field theory
Index
Color Plates
Chapter 1: A short history of SPM
Chapter 2: Statistical parametric mapping
Chapter 3: Modelling brain responses
Part 2: Computational anatomy
Chapter 4: Rigid Body Registration
Chapter 5: Non-linear Registration
Chapter 6: Segmentation
Chapter 7: Voxel-Based Morphometry
Part 3: General linear models
Chapter 8: The General Linear Model
Chapter 9: Contrasts and Classical Inference
Chapter 10: Covariance Components
Chapter 11: Hierarchical Models
Chapter 12: Random Effects Analysis
Chapter 13: Analysis of Variance
Chapter 14: Convolution Models for fMRI
Chapter 15: Efficient Experimental Design for fMRI
Chapter 16: Hierarchical models for EEG and MEG
Part 4: Classical inference
Chapter 17: Parametric procedures
Chapter 18: Random Field Theory
Chapter 19: Topological Inference
Chapter 20: False Discovery Rate procedures
Chapter 21: Non-parametric procedures
Part 5: Bayesian inference
Chapter 22: Empirical Bayes and hierarchical models
Chapter 23: Posterior probability maps
Chapter 24: Variational Bayes
Chapter 25: Spatio-temporal models for fMRI
Chapter 26: Spatio-temporal models for EEG
Part 6: Biophysical models
Chapter 27: Forward models for fMRI
Chapter 28: Forward models for EEG
Chapter 29: Bayesian inversion of EEG models
Chapter 30: Bayesian inversion for induced responses
Chapter 31: Neuronal models of ensemble dynamics
Chapter 32: Neuronal models of energetics
Chapter 33: Neuronal models of EEG and MEG
Chapter 34: Bayesian inversion of dynamic models
Chapter 35: Bayesian model selection and averaging
Part 7: Connectivity
Chapter 36: Functional integration
Chapter 37: Functional connectivity: eigenimages and multivariate analyses
Chapter 38: Effective Connectivity
Chapter 39: Non-linear coupling and kernels
Chapter 40: Multivariate autoregressive models
Chapter 41: Dynamic Causal Models for fMRI
Chapter 42: Dynamic causal models for EEG
Chapter 43: Dynamic Causal Models and Bayesian selection
Appendices
Linear models and inference
Dynamical systems
Expectation maximization
Variational Bayes under the Laplace approximation
Kalman filtering
Random field theory
Index
Color Plates